Download Advances in Artificial Intelligence: 23rd Canadian by Dekang Lin (auth.), Atefeh Farzindar, Vlado Kešelj (eds.) PDF

By Dekang Lin (auth.), Atefeh Farzindar, Vlado Kešelj (eds.)

This ebook constitutes the refereed lawsuits of the twenty third convention on man made Intelligence, Canadian AI 2010, held in Ottawa, Canada, in May/June 2010. The 22 revised complete papers awarded including 26 revised brief papers, 12 papers from the graduate pupil symposium and the abstracts of three keynote shows have been conscientiously reviewed and chosen from ninety submissions. The papers are equipped in topical sections on textual content category; textual content summarization and IR; reasoning and e-commerce; probabilistic computer studying; neural networks and swarm optimization; computing device studying and knowledge mining; average language processing; textual content analytics; reasoning and making plans; e-commerce; semantic net; computing device studying; and information mining.

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Journal of Environmental Management 85, 338–344 (2007) 8. : Rough set based decision model in information retrieval and filtering. In: Third World Multiconference on Systemics, Cybernetics and Informatics (SCI 1999) and Fifth International Conference on Information Systems Analysis and Synthesis (ISAS 1999), vol. 5, pp. 398–403 (1999) 9. : Classifying news stories using memory based reasoning. In: Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval, pp.

25) 36 B. Zhou, Y. Yao, and J. Luo Table 1. Three-way decision results with λ = 1 accept further-exam Reject Total Actually legitimate Actually spam 465 28 22 13 12 227 499 268 Total 493 35 239 767 Table 2. Naive Bayesian results with λ = 1 Classified legitimate Classified spam Total 5 Actually legitimate Actually spam 476 32 23 236 499 268 Total 508 259 767 Experimental Results and Evaluations Our experiments were performed on a spambase data set from UCI Machine Learning Repository [10]. The data set consists of 4601 instances, with 1813 instances as spam, and 2788 instances as legitimate, each instance is described by 58 attributes.

P r(x) (3) Similarly, the corresponding probabilities P r(C c |x) of an email being in the spam class given vector x can be reformulated as: P r(C c |x) = P r(C c ) n i=1 P r(xi |C c ) . P r(x) (4) Note that P r(x) in equation (3) and (4) is unimportant with regard to making a decision. It is basically a scale factor that assures P r(C|x) + P r(C c |x) = 1. This scale factor can be eliminated by taking the ratio of P r(C|x) and P r(C c |x): n P r(C|x) P r(xi |C) P r(C) = . c P r(C |x) i=1 P r(xi |C c ) P r(C c ) (5) A Three-Way Decision Approach to Email Spam Filtering 31 P r(C|x)/P r(C c |x) is called the posterior odds of an email being in the legitimate class against being in the spam class given x.

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